Thematic data on desertification (land desertification, salinization and vegetation degradation) in Central Asia, includes three parts: Distribution Map of Sandy Land in Central Asia, Distribution Map of Salinized Land in Central Asia and Distribution Map of Land Vegetation Degradation in Central Asia. The spatial resolution of the data is 1km, the time resolution is in 2015. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
XU Wenqiang
Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
The data was obtained from the 30-second global elevation dataset developed by the US Geological Survey (USGS) and completed in 1996. Downloaded the data from the NCAR and UCAR Joint Data Download Center (https://rda.ucar.edu/datasets/ds758.0/) and redistributed it through this data center. GTOPO30 divides the world into 33 blocks. The sampling interval is 30 arc seconds, which is 0.00833333333333333 degrees. The coordinate reference is WGS84. The DEM is the distance from the sea level in the vertical direction, ie the altitude, in m, the altitude range from -407 to 8752, the ocean depth information is not included here, the negative value is the altitude of the continental shelf; the ocean is marked as -9999, the elevation above the coastline is at least 1; the island less than 1 square kilometer is not considered. In order to facilitate the user's convenience, on the basis of the block data, splice 10 blocks in -10S-90N and 20W-180E without any resampling processing. This data file is DEM_ptpe_Gtopo30.nc
HE Yongli
The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. Based on pixel dichotomy model, the vegetation coverage model is developed by using multi-scale remote sensing images, combining with high precision remote sensing parameters such as vegetation community type and distribution characteristics, and the mixed pixel decomposition method is used to construct the vegetation coverage model. All data could be used only after the permission of the data distributor.
LIU Wenjun
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
GAO Xin
Wildfires can strongly affect the frozen soil environment by burning surface vegetation and soil organic matter. Vegetation affected by fire can take many years to return to mature pre-fire levels. In this data set, the effects of fires on vegetation regrowth in a frozen-ground tundra environment in the Anaktuvuk River Basin on the North Slope of Alaska were studied by quantifying changes in C-band and L-band SAR backscatter data over 15 years (2002-2017). After the fire, the C- and L-band backscattering coefficients increased by 5.5 and 4.4 dB, respectively, in the severe fire area compared to the unburned area. Five years after the fire, the difference in C-band backscattering between the fire zone and the unburned zone decreased, indicating that the post-fire vegetation level had recovered to the level of the unburned zone. This long recovery time is longer than the 3-year recovery estimated from visible wavelength-based NDVI observations. In addition, after 10 years of vegetation recovery, the backscattering of the L-band in the severe fire zone remains approximately 2 dB higher than that of the unburned zone. This continued difference may be caused by an increase in surface roughness. Our analysis shows that long-term SAR backscattering data sets can quantify vegetation recovery after fire in an Arctic tundra environment and can also be used to supplement visible-wavelength observations. The temporal coverage of the backscattering data is from 2002 to 2017, with a time resolution of one month, and the data cover the Anaktuvuk River area on the North Slope of Alaska. The spatial resolution is 30~100 m, the C- and L-band data are separated, and a GeoTIFF file is stored every month. For details on the data, see SAR Backscattering Data of the Anaktuvuk River Basin on the North Slope of Alaska - Data Description.
JIANG Liming
The data set includes estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on the MODIS 16-day synthetic NDVI product (MOD13A2 collection 6). Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 2001 to 2014, and the spatial resolution is 1 km.
WANG Xufeng
The data set contains NPP products data produced by the maximum synthesis method of the three source regions of the Yellow River, the Yangtze River and the Lancang River. The data of remote sensing products MOD13Q1, MOD17A2, and MOD17A2H are available on the NASA website (http://modis.gsfc.nasa.gov/). The MOD13Q1 product is a 16-d synthetic product with a resolution of 250 m. The MOD17A2 and MOD17A2H product data are 8-d synthetic products, the resolution of MOD17A2 is 1 000 m, and the resolution of MOD17A2H is 500 m. The final synthetic NPP product of MODIS has a resolution of 1 km. The downloaded MOD13Q1, MOD17A2, and MOD17A2H remote sensing data products are in HDF format. The data have been processed by atmospheric correction, radiation correction, geometric correction, and cloud removal. 1) MRT projection conversion. Convert the format and projection of the downloaded data product, convert the HDF format to TIFF format, convert the projection to the UTM projection, and output NDVI with a resolution of 250 m, EVI with a resolution 250 m, and PSNnet with resolutions of 1 000 m and 500 m. 2) MVC maximum synthesis. Synthesize NDVI, EVI, and PSNnet synchronized with the ground measured data by the maximum value to obtain values corresponding to the measured data. The maximum synthesis method can effectively reduce the effects of clouds, the atmosphere, and solar elevation angles. 3) NPP annual value generated from the NASA-CASA model.
Kamel Didan*, Armando Barreto Munoz, Ramon Solano, Alfredo Huete
The data set includes the estimated data of the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on 10-day synthetic NDVI products from the SPOT satellite. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage is from 1999 to 2013, and the spatial resolution is 1 km.
WANG Xufeng
The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.
WANG Xufeng
These data contain two data files: GLOBELAND30 TILES (raw data) and TIBET_ GLOBELAND30_MOSAIC (mosaic data). The raw data were downloaded from the Global Land Cover Data website (GlobalLand3) (http://www.globallandcover.com) and cover the Tibetan Plateau and surrounding areas. The raw data were stored in frames, and for the convenience of using the data, we use Erdas software to splice and mosaic the raw data. The Global Land Cover Data (GlobalLand30) is the result of the “Global Land Cover Remote Sensing Mapping and Key Technology Research”, which is a key project of the National 863 Program. Using the American Landsat images (TM5, ETM+) and Chinese Environmental Disaster Reduction Satellite images (HJ-1), the data were extracted by a comprehensive method based on pixel classification-object extraction-knowledge checks. The data include 10 primary land cover types—cultivated land, forest, grassland, shrub, wetland, water body, tundra, man-made cover, bare land, glacier and permanent snow—without extracting secondary types. In terms of accuracy assessment, nine types and more than 150,000 test samples were evaluated. The overall accuracy of the GlobeLand30-2010 data is 80.33%. The Kappa indicator is 0.75. The GlobeLand30 data use the WGS84 coordinate system, UTM projection, and 6-degree banding, and the reference ellipsoid is the WGS 84 ellipsoid. According to different latitudes, the data are organized into two types of framing. In the regions of 60° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 6° (longitude); in the regions of 60° to 80° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 12° (longitude). The framing is projected according to the central meridian of the odd 6° band. GLOBELAND30 TILES: The original, unprocessed raw data are retained. TIBET_ GLOBELAND30_MOSAIC: The Erdas software is used to mosaic the raw data. The parameter settings use the default value of the raw data to retain the original, and the accuracy is consistent with that of the downloading site.
CHEN Jun
There are three types of glacial lakes: supraglacial lakes, lakes attached to the end of the glacier and lakes not attached to the end of the glacier. Based on this classification, the following properties are studied: the variation in the number and area of glacial lakes in different basins in the Third Pole region, the changes in extent in terms of size and area, distance from glaciers, the differences in area changes between lakes with and without the supply of glacial melt water runoff, the characteristics of changes in the glacial lake area with respect to elevation, etc. Data source: Landsat TM/ETM+ 1990, 2000, 2010. The data were visually interpreted, which included checking and editing by comparing the original image with Google Earth images when the area was greater than 0.003 square kilometres. The data were applied to glacial lake changes and glacial lake outburst flood assessments in the Third Pole region. Data type: Vector data. Projected Coordinate System: Albers Conical Equal Area.
ZHANG Guoqing
This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.
LI Fei, Fei Li, Zhijun Zhang
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
NASA
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
The National Center for Atmospheric Research
The global Cryosat-2 GDR dataset is generated by the European Space Agency (ESA); it has a temporal coverage from 2010 to 2016 and covers the globe. On April 8, 2010, the ESA launched the Cryosat-2 high-tilt polar orbit satellite. The satellite is equipped with an SAR Interferometer Radar Altimeter (SIRAL), which is mainly used to monitor polar ice thickness and sea ice thickness changes, and, furthermore, to study the effects of melting polar ice on global sea level rise and that of global climate change on Antarctic ice thickness. The altimeter operates in the Ku-band and at a frequency of 13.575 GHz, it includes three measurement modes. One is a low-resolution altimeter measurement mode (LRM) that points to the subsatellite point to obtain all surface observations for land, sea, and ice sheets; its processing is similar to ENVISAT/RA-2, with an orbital resolution of 5 to 7 km. The second is the Synthetic Aperture Radar (SAR) measurement mode, which is mainly used to improve the accuracy and resolution of sea ice observations; it can make the resolution along the orbit reach approximately 250 m. The third is the Interferometric Synthetic Aperture Radar (InSAR), which is mainly used to improve the accuracy of areas with complex terrain such as the edges of ice sheets or ice shelves. The CryoSat -2/SIRAL data products mainly include 0-level data, 1b-level data, 2-level data and high-level data. The Cryosat-2/SIRAL products consist of two files: an XML head file (.HDR) and a data product file (.DBL). The HDR file is an auxiliary ASCII file for fast identification and retrieval of the data files. 1b-level products are stored separately according to the measurement modes, and the data recording formats of different modes are also different. Each waveform in LRM mode and SAR mode has 128 sampling points, while that in SARIn mode has 512 sampling points. 2-level GDR products are available for most scientific applications, including measurement time, geographic location, altitude, and more. In addition, the altitude information in GDR products has been obtained through instrumental calibration, transmission delay corrections, geometric corrections, and geophysical corrections (such as atmospheric corrections and tidal corrections). The GDR products are single global full-track data, that is, the measurement results of the three modes. After different processing, they are combined in chronological order; thereby, the data recording formats are unified. The data in the three modes use different waveform retracking algorithms to obtain altitude values. In the latest updated Baseline C data, the LRM mode data use three algorithms: Refined CFI, UCL and Refined OCOG.
SHEN Guozhuang, FU Wenxue
The Sentinel-1A/B satellite uses a near-polar sun-synchronous orbit with an orbital altitude of 693 km, an orbital inclination of 98.18°, and an orbital period of 99 minutes. It is equipped with a C-band Synthetic Aperture Radar (SAR) with a designed service life of 7 years (12 years expected). Sentinel-l has a variety of imaging methods that enable different polarization modes such as single-polarization and dual-polarization. Sentinel-1A SAR has four working modes: Strip Map Mode (SM), Extra Wide Swath (EW), Interferometric Wide Swath (IW) and Wave Mode (WV). Satellite A was successfully launched in April 2014. The revisit period of the same region was 12 days. Satellite B successfully operated on orbit in April 2016. The current revisiting period reached 3 to 6 days. After the operation of two satellites, the S1 data acquisition frequency in the Antarctic region increased greatly. This data set comprises the Sentinel-1 SAR data for the Antarctic ice sheet and the Greenland Ice Sheet area. The data band comprises C-band extra wide multiview data with a resolution of 20 m*40 m. The temporal resolution is 12 days and is related to the round-trip period, the width is 400 km, the noise level is -25 dB, and the radiation measurement accuracy is 1.0 dB. The annual temporal coverage of these data is October to the next March in the Antarctic and April to September in Greenland, and the spatial coverage comprises the Antarctic ice sheet ice shelf area and Greenland ice sheet.
Lu Zhang
The continuous advancement of SAR interferometry technology makes it possible to obtain multitemporal DEMs with high precision in the glacial area. In particular, in 2000, the Shuttle Radar Topography Mission (SRTM) led by NASA provided DEM data covering the area from 56ºS to 60ºN; the TanDEM-X bistatic SAR interferometry system of DLR could provide the global DEM data with high resolution and precision. These high-quality, large-coverage SAR interferometry data, as well as published DEM data products, provided valuable information for using the multitemporal DEMs to detect changes in ice thickness. The temporal coverage of the ice thickness variation data of typical glaciers on the Tibetan Plateau was from 2000 to 2013, covering Puruogangri and the west Qilian Mountains with a spatial resolution of 30 meters. Using TanDEM-X bistatic InSAR data and a C-band SRTM DEM, the differential radar interferometry method was first used to generate a TanDEM-X DEM with high precision. Then, based on the precise registration of DEM, the DEM data obtained in different periods were compared. Lastly, the ice thickness changes were estimated. The format of the data set was GeoTIFF, and each typical glacier ice thickness change was stored in a folder. For details of the data, please refer to the Ice elevation changes for typical glaciers on the Tibetan Plateau - Data Description.
JIANG Liming
This data set contains the wide swath mode Level 1B SAR data acquired over Greenland in 2005 by the ASAR sensor of the ENVISAT-1 satellite. The width is 400 km, the spatial resolution is 75 m, and the absolute positioning accuracy is approximately 200 m. The SAR data are stored in a time-growth order, which causes the images of the descending track to be left-right mirror images and the images of the ascending track to be up-down images. The naming scheme for these data is as follows: ASA_IMS_1PPIPA 20050402_095556_000000162036_00065_16151_0388.N1 ASA: Product identification, ASAR Sensor IMS: Reception and processing information of the data (imaging modes, such as WS, WSS, IM, ...) 1PPIPA: Customized number 20050402: Acquisition time of the data (UTC time) 095556: Geographic location (start, end) 000000162036: Information on the satellite orbit 00065: Product trust data 16151: Size and structure information of the product 0388 => Check code
HUI Fengming
The 1 km / 5-day Lai data set of Heihe River basin provides the 5-day Lai synthesis results of 2010-2014. The data uses Terra / MODIS, Aqua / MODIS, as well as domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
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